Inter-trial convergence of neural task states supports cognitive flexibility

The ability to switch between tasks is a core component of human intelligence, yet a mechanistic understanding of this capacity has remained elusive. Long-standing debates over how task switching is influenced by preparation for upcoming tasks or interference from previous tasks have been difficult to resolve without quantitative neural predictions. We advance this debate by using state-space modeling to directly compare the latent task dynamics in task-optimized recurrent neural networks and human electroencephalographic recordings. Over the inter-trial interval, both neural networks and brains reset into a neutral task state, an effective dynamic motif that reconciles the roles of preparation and interference in task switching. These findings provide a quantitative account of cognitive flexibility and a promising paradigm for bridging artificial and biological neural networks.